Predicted individual's obesity status using random forest model on dataset of 29 numerical and categorical predictors. We simplified our final model with 10 predictors, resulting in full accuracy and low misclassification rate (0.017). Our significant predictors were height, age, and daily water intake for obesity. This project demonstrates the potential of predictive modeling in addressing global health challenges related to obesity and chronic diseases.
Examining the Effects of Exercise and Dark Chocolate Consumption on Blood Endorphin Levels: A Virtual Study Across Age Groups (2024) [paper]
Investigated the combined effects of exercise and varying cocoa content in chocolate on endorphin levels in women across different age groups. Using a two-way randomized block design, 162 virtual participants from The Islands online resource were divided into six treatment conditions involving combinations of no exercise or light jogging and consuming no chocolate, chocolate with 40% cocoa, or chocolate with 99% cocoa. Blood endorphin levels were measured after the treatments. ANOVA results revealed significant individual effects of both exercise and chocolate on endorphin levels (p = 0.009 and p < 2.44e-45, respectively), though no significant interaction between the two factors or age-related differences was observed. Tukey’s HSD test further supported the independent influence of exercise and chocolate. While no interaction effect or age-based differences were found, the study reinforces the benefits of exercise and dark chocolate on endorphin levels.
I used linear regression model to predict the amount of time it takes to get somewhere from Uber based on the starting and ending longitude and latitude, using Jupyter Python. By extracting the Euclidean distance between the given longitude and latitude variables and splitting the data to train the model and predict using the test data, I was able to generate an accurate model that predicts the time based off of the Euclidean distance. The folder contains the the dataset, Jupyter notebook file, and the predictions cvs file.
Analyzed the eviction of the Chavez Ravine community in Los Angeles by focusing on the conflict between local residents and the city government. The government used eminent domain to clear the area for the construction of Dodger Stadium, highlighting how the government prioritized economic interests, such as tourism and private development, over the needs of the displaced residents who lived there for generations. We examined the protests in response to the evictions through conflict mapping, an empirical framework, to then evaluate the violent event through three analytical lenses: urbicide, trias violentiae, and life cycle model. By showcasing the struggle against the long-term social and economic consequences of urban redevelopment through frameworks from literature on urban violence, we contributed to the ongoing discussion of displacement and discrimination that are often times faced by low-income, marginalized households in Los Angeles.
I examined Haiti's persistent vulnerability to natural disasters through the interplay between environmental risks, political instability, and economic underdevelopment to propose policy solution on Municipal Civil Protection Committees (CCPC) under the World Bank. I introduced the cyclical nature of poverty and disaster susceptibility, where the geography and existing inequalities of Haiti exacerbate the impacts of natural disasters. This lead to significant recovery needs, particularly in the social sector, which short-term assistance provided by international organizations does not suffice. In extending supplies, reorganizing training camps, and providing alternative incentives to the CCPC, I emphasized the necessity for long-term institutional development and resilience through community engagement and resource investment aimed at enhancing disaster preparedness. I also identified political constraints, consequences, and other potential alternatives of the policy design before presenting evaluation and implementation methods of the CCPC intervention program.
The goal of our experiment is to try to find the most effective way to reduce screen time due to the negative mental health effects a prolonged exposure to social media on anxiety, depression, and productivity. We targeted two secondary outcomes to find which optimally addresses our behavioral problem: the potential lack of knowledge on how to reduce screen time or targeting the beliefs people may have on how social media impacts the body and mind. We will have one treatment and control email, with the treatment targeting knowledge and beliefs and control having no intervention. Our results shows a significant treated estimate (2.45), but our small sample size undermined the significance of the estimate. Since we cannot fully analyze our given data to its potential, we can only assume to adopt neither video treatments.
I researched on how student workers are exploited within their campus workplace environment and how it corresponds to the university’s responsibility. As academic workers across University of California schools strike for a fair contract against unfair labor practices, the student workers of minimum wage service jobs with more physical labor conditions, such as at food courts, bookstores, or transportation services, have the same sentiments but less competency to reach a fair bargain. The basis of understanding exploitation is gathered through Michael Yates’s Why Unions Matter on how “the most fundamental aspect of work is not controlled by us” because “employers try to organize their workplaces so that we cannot exert much control by our own actions” (p.8). After conducting an interview with a student worker from Boston College's bookstore, I proposed that student workers are exploited through the manipulation of their hours and physical well being because of the disparities in labor relations and the institutionalized structure of universities that perpetuates the extremities of stratification.